Replay Is Not Reality: Embodied RL When Both Policy and Physics Shift
Abstract Embodied agents must continuously adapt to the physical world using interaction data collected across varying timescales, controllers, and environmental conditions. However, standard reinforcement learning assumes stationary dynamics and on-policy data collection, which breaks down when agents must learn from heterogeneous data sources with non-stationary dynamics and off-policy behavior. In this episode of Embodied AI 101, we explore "Replay Is Not Reality: Embodied RL When Both Policy and Physics Shift". We break down the research, methodology, and real-world implications for…
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